Building an Enterprise Chatbot: Work with Protected Enterprise Data Using Open Source Frameworks
暫譯: 建立企業聊天機器人：使用開源框架處理受保護的企業數據
Singh, Abhishek, Ramasubramanian, Karthik, Shivam, Shrey
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商品描述
In the next sections, you'll design and implement the backend framework of a typical chatbot from scratch. You will also explore some popular open-source chatbot frameworks such as Dialogflow and LUIS. The authors then explain how you can integrate various third-party services and enterprise databases with the custom chatbot framework. In the final section, you'll discuss how to deploy the custom chatbot framework on the AWS cloud.
By the end of Building an Enterprise Chatbot, you will be able to design and develop an enterprise-ready conversational chatbot using an open source development platform to serve the end user.
What You Will Learn
- Identify business processes where chatbots could be used
- Focus on building a chatbot for one industry and one use-case rather than building a ubiquitous and generic chatbot
- Design the solution architecture for a chatbot
- Integrate chatbots with internal data sources using APIs
- Discover the differences between natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG)
- Work with deployment and continuous improvement through representational learning
Who This Book Is ForData scientists and enterprise architects who are currently looking to deploy chatbot solutions to their business.
商品描述(中文翻譯)
探索聊天機器人在商業中的應用，專注於聊天機器人的設計、部署和持續改進，並以銀行和保險行業的一個具體案例為例。本書首先識別銀行和保險行業中的商業流程。這涉及從客戶服務中心的對話、在線聊天、電子郵件及其他自然語言處理（NLP）來源收集數據。接著，您將設計聊天機器人的解決方案架構。一旦架構確定，作者將解釋自然語言理解（NLU）、自然語言處理（NLP）和自然語言生成（NLG），並提供示例。
在接下來的部分中，您將從零開始設計和實現一個典型聊天機器人的後端框架。您還將探索一些流行的開源聊天機器人框架，如 Dialogflow 和 LUIS。然後，作者將解釋如何將各種第三方服務和企業數據庫與自定義聊天機器人框架集成。在最後一部分，您將討論如何在 AWS 雲上部署自定義聊天機器人框架。
在《建立企業聊天機器人》結束時，您將能夠使用開源開發平台設計和開發一個適合企業的對話式聊天機器人，以服務最終用戶。
您將學到的內容：
- 識別可以使用聊天機器人的商業流程
- 專注於為一個行業和一個用例構建聊天機器人，而不是構建一個普遍且通用的聊天機器人
- 設計聊天機器人的解決方案架構
- 使用 API 將聊天機器人與內部數據源集成
- 發現自然語言理解（NLU）、自然語言處理（NLP）和自然語言生成（NLG）之間的差異
- 通過表徵學習進行部署和持續改進
本書適合對象：
數據科學家和企業架構師，正在尋求將聊天機器人解決方案部署到其業務中的人員。
作者簡介
Abhishek Singh is on a mission to profess the de facto language of this millennium, the numbers. He is on a journey to bring machines closer to humans, for a better and more beautiful world by generating opportunities with artificial intelligence and machine learning. He leads a team of data science professionals solving pressing problems in food security, cyber security, natural disasters, healthcare, and many more areas, all with the help of data and technology. Abhishek is in the process of bringing smart IoT devices to smaller cities in India so that people can leverage technology for the betterment of life.
He has worked with colleagues from many parts of the United States, Europe, and Asia, and strives to work with more people from various backgrounds. In 7 years at big corporations, he has stress-tested the assets of U.S. banks at Deloitte, solved insurance pricing models at Prudential, and made telecom experiences easier for customers at Celcom, and core SaaS Data products at Probyto. He is now creating data science opportunities with his team of young minds.
He actively participates in analytics-related thought leadership, authoring, public speaking, meetups, and training in data science. He is a staunch supporter of responsible use of AI to remove biases and fair use of AI for a better society.
Abhishek completed his MBA from IIM Bangalore, a B.Tech. In Mathematics and Computing from IITGuwahati, and a PG Diploma in Cyber Law from NALSAR University, Hyderabad.
Karthik Ramasubramanian has over seven years of practice and leading Data Science and Business Analytics in Retail, FMCG, E-Commerce, Information Technology for a multi-national and two unicorn startups. A researcher and problem solver with a diverse set of experience in the data science lifecycle, starting from a data problem discovery to creating a data science prototype/product.
On the descriptive side of data science, designed, developed and spearheaded many A/B experiment frameworks for improving product features, conceptualized funnel analysis for understanding user interactions and identifying the friction points within a product, designing statistically robust metrics and visual dashboards. On the predictive side, developed intelligent chatbots which understand human-like interactions, customer segmentation models, recommendation systems, identifying medical specialization from a patient query for telemedicine, and many more. He actively participates in analytics related thought leadership, authoring blogs & books, public speaking, meet-ups, and training & mentoring for Data Science. Karthik completed his M.Sc. in Theoretical Computer Science at PSG College of Technology, India, where he pioneered the application of machine learning, data mining, and fuzzy logic in his research work on the computer and network security.Shrey Shivam extensive experience in leading the design, development, and delivery of solutions in the field of data engineering, stream analytics, machine learning, graph databases, and natural language processing. In his seven years of experience, he has worked with various conglomerates, startups, and big corporations and has gained relevant exposure to digital media, e-commerce, investment banking, insurance, and a suite of transaction-led marketplaces across music, food, lifestyle, news, legal and travel.
He is a keen learner and is actively engaged in designing the next generation of systems powered by artificial intelligence-based analytical and predictive models. He has taken up various roles in product management, data analytics, digital growth, system architecture, and full stack engineering. In the era of rapid acceptance and adoption of new and emerging technologies, he believes in strong technical fundamentals and advocates continuous improvement through self-learning.
Shrey is currently leading a team of machine learning & big data engineers across the US, Europe, and India to build robust and scalable big data pipelines to implement various statistical and predictive models. Shrey has completed his BTech in Information Technology from Cochin University of Science Technology, India.
作者簡介(中文翻譯)
阿比謝克·辛格的使命是宣揚這個千禧年的事實語言——數字。他正在努力使機器更接近人類，通過人工智慧和機器學習創造機會，為一個更美好、更美麗的世界而奮鬥。他領導著一支數據科學專業團隊，解決食品安全、網絡安全、自然災害、醫療保健等多個領域的緊迫問題，所有這些都依賴於數據和技術的幫助。阿比謝克正在將智能物聯網設備引入印度的小城市，讓人們能夠利用技術改善生活。
他曾與來自美國、歐洲和亞洲的同事合作，並努力與來自不同背景的更多人合作。在大型企業工作的七年中，他在德勤對美國銀行的資產進行壓力測試，在保誠解決保險定價模型，並在Celcom為客戶簡化電信體驗，以及在Probyto開發核心SaaS數據產品。他現在正與他的年輕團隊一起創造數據科學的機會。
他積極參與與分析相關的思想領導，撰寫文章、公開演講、舉辦聚會和數據科學培訓。他是負責任使用人工智慧的堅定支持者，致力於消除偏見並公平使用人工智慧以促進更好的社會。
阿比謝克在印度班加羅爾的IIM完成了MBA，在印度Guwahati的IIT獲得數學與計算的B.Tech學位，並在海德拉巴的NALSAR大學獲得網絡法律的PG文憑。
卡爾提克·拉馬蘇布拉馬尼安在零售、快速消費品、電子商務和信息技術領域擁有超過七年的數據科學和商業分析實踐及領導經驗，曾在一家跨國公司和兩家獨角獸初創公司工作。他是一位研究者和問題解決者，擁有數據科學生命周期的多樣經驗，從數據問題的發現到創建數據科學原型/產品。
在數據科學的描述性方面，他設計、開發並主導了多個A/B實驗框架，以改善產品特徵，構思漏斗分析以理解用戶互動並識別產品中的摩擦點，設計統計上穩健的指標和可視化儀表板。在預測性方面，他開發了能理解類人互動的智能聊天機器人、客戶細分模型、推薦系統、從患者查詢中識別遠程醫療的醫療專業化等。
他積極參與與分析相關的思想領導，撰寫博客和書籍、公開演講、舉辦聚會以及數據科學的培訓和指導。
卡爾提克在印度PSG科技學院完成了理論計算機科學的碩士學位，並在他的研究工作中開創性地應用了機器學習、數據挖掘和模糊邏輯於計算機和網絡安全。
施瑞·希瓦姆在數據工程、流分析、機器學習、圖形數據庫和自然語言處理領域擁有豐富的設計、開發和交付解決方案的經驗。在七年的工作經驗中，他曾與各種企業、初創公司和大型企業合作，並在數字媒體、電子商務、投資銀行、保險以及音樂、食品、生活方式、新聞、法律和旅行等交易導向市場中獲得了相關的曝光。
他是一位熱衷學習者，積極參與設計下一代由人工智慧驅動的分析和預測模型的系統。他在產品管理、數據分析、數字增長、系統架構和全棧工程等多個角色中發揮作用。在新興技術快速接受和採用的時代，他相信堅實的技術基礎，並倡導通過自我學習持續改進。
施瑞目前正在領導一支由機器學習和大數據工程師組成的團隊，遍布美國、歐洲和印度，建立穩健且可擴展的大數據管道，以實施各種統計和預測模型。施瑞在印度科欽科技大學完成了信息技術的BTech學位。
